Objective: Little Tunny Euthynnus alletteratus is a commercially viable species, and its research has been prioritized by the International Commission for the Conservation of Atlantic Tunas. This study aimed to evaluate the stock status of Little Tunny in the Northeast Atlantic Ocean, the effectiveness of the life history priors, and the sensitivity of the model's predicted reference points to prior misspecification. The findings could help reduce uncertainties regarding the state of Little Tunny in the region. Methods: We used empirically estimated life history parameters and purse-seine fish aggregating device (FAD) and free school fishing length-frequency data from 2000 to 2021, available in the International Commission for the Conservation of Atlantic Tunas database. We applied the length-based Bayesian biomass (LBB) and length-based spawning potential ratio assessment methods. Result: The LBB's outputs indicate that Little Tunny was grossly overfished at the exerted fishing pressure. This was corroborated by length-based spawning potential ratio estimates of low spawning per recruit for the stock exploited by purse seines under FAD and free school sets. The reference points of both models were most sensitive to asymptotic length (L∞) prior misspecification, and including life history priors in LBB produced a narrower confidence interval of the estimated reference points. Conclusion: The Northeast Atlantic Ocean Little Tunny fishery exploited by purse-seine fisheries under FAD and free school sets may be unsustainable. We maintain that user priors could reduce uncertainty in LBB, whereas accurate specification of priors, particularly L∞, is required to have less biased results using the models. We recommend catch limits through, for instance, spatial closures, but caution is advised due to uncertainty in this study's life history priors.

This study was conducted in the lower, middle and upper reaches of the Rokel/Seli River, Northern Sierra Leone. There is a research gap on the growth or wellbeing of tilapia fish species in Sierra Leone. The objective of this study is to determine the length-weight relationship and condition factor of Coelotilapia joka from the main river channel. This research could contribute to promoting an agenda for fish culture practices for the fish. A total of 228 specimens of Coelotilapia joka were collected using gill nets. Fish length and weight measurements were done using fish measuring board and digital weighing balance respectively. Measurement for water quality was made using the Jemway multi-parameter water quality measuring device (Model: J970, Barloworld Scientific, United Kingdom). The values for regression coefficient “b” gave 2.5437, 2.1841 and 2.4585 for males, females and combined sex respectively, implying a negative allometric growth in the fish at (b1). The difference in ‘K” for both sex is significant (p<0.05). A study for a period of one year could, however, provide a complete analysis of seasonal variations in growth and condition of the fish.

Length-based stock assessment methods are widely applied in data-limited fisheries, yet the effects of how length-frequency data are temporally grouped prior to analysis remain poorly examined. Temporal grouping is routinely used to increase sample size and approximate equilibrium conditions, but it may also alter the size structure presented to assessment models and bias inference. In this study, we evaluate how alternative temporal grouping schemes influence stock status inference within a single length-based framework, using the length-based spawning potential ratio (LBSPR) model as a diagnostic tool. Using a 30-year length-frequency dataset from a tropical purse seine fishery in the Northeast Atlantic as an illustrative case, we applied LBSPR under four practice-relevant temporal grouping schemes: full-period pooling, a broad regime-based scheme, decadal blocks, and five-year blocks. Life history parameters and model settings were held constant across schemes to isolate the effect of temporal grouping. A sensitivity analysis of biological parameters was conducted for the finest temporal scheme to contextualise robustness. Results show that temporal grouping alone can substantially alter the inferred status of the illustrative case. The fully pooled scheme produced an apparently favourable status signal, whereas finer temporal groupings revealed extended periods of inferred reproductive depletion, followed by a more recent recovery. Sensitivity analyses indicate that, while biological parameter uncertainty influences the magnitude of estimates, it does not overturn the dominant effect of temporal grouping on inferred status patterns. This study demonstrates that temporal grouping is not a neutral preprocessing step but a structural decision with the potential to conceal or reveal exploitation signals in length-based assessments. We argue that temporal grouping should be treated as an explicit sensitivity dimension in data-limited assessment workflows. By shifting attention from stock-specific outcomes to data-structuring choices, this work provides practical guidance for improving transparency and robustness in length-based stock status inference.

This study on Trachurus trachurus used data from the Regional Committee (CECAF). It employs JABBA-Select, a model that balances age-structured and traditional surplus production models, uniquely incorporating life history and fishery selectivity. Sensitivity scenarios included single and combined catch per unit effort (CPUE) data (1999-2017) for demersal and pelagic fisheries under logistic and dome-shaped selectivity functions. Results indicated that the demersal CPUE under logistic selectivity performed best, leading to a single-fleet analysis as the base case. Model results were robust against variations in natural mortality (M) compared to other input parameters, including steepness (h), while stock status perceptions remained consistent. The base case revealed an overexploited stock (SB/SBMSY = 66%) with a slow growth rate (r = 29%) in 2017. Catches exceeding 15,744 tons from 2022 to 2030 may not be sustainable. JABBA-Select is recommended for future assessments; however, careful consideration of input parameters is essential to minimize uncertainties.